
"""
This file is derived from [torchsparse](https://github.com/mit-han-lab/torchsparse).
Modified for [PlanarRecon](https://github.com/neu-vi/PlanarRecon) by Yiming Xie.

Original License:
MIT License

Copyright (c) 2020-2021 TorchSparse Contributors

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
"""

import torch
import torchsparse.nn.functional as F
from torchsparse import PointTensor, SparseTensor
from torchsparse.nn.utils import get_kernel_offsets

__all__ = ['initial_voxelize', 'point_to_voxel', 'voxel_to_point']


# z: PointTensor
# return: SparseTensor
def initial_voxelize(z, init_res, after_res):
    new_float_coord = torch.cat(
        [(z.C[:, :3] * init_res) / after_res, z.C[:, -1].view(-1, 1)], 1)

    pc_hash = F.sphash(torch.floor(new_float_coord).int())
    sparse_hash = torch.unique(pc_hash)
    idx_query = F.sphashquery(pc_hash, sparse_hash)
    counts = F.spcount(idx_query.int(), len(sparse_hash))

    inserted_coords = F.spvoxelize(torch.floor(new_float_coord), idx_query,
                                   counts)
    inserted_coords = torch.round(inserted_coords).int()
    inserted_feat = F.spvoxelize(z.F, idx_query, counts)

    new_tensor = SparseTensor(inserted_feat, inserted_coords, 1)
    new_tensor.cmaps.setdefault(new_tensor.stride, new_tensor.coords)
    z.additional_features['idx_query'][1] = idx_query
    z.additional_features['counts'][1] = counts
    z.C = new_float_coord

    return new_tensor


# x: SparseTensor, z: PointTensor
# return: SparseTensor
def point_to_voxel(x, z):
    if z.additional_features is None or z.additional_features.get('idx_query') is None\
       or z.additional_features['idx_query'].get(x.s) is None:
        #pc_hash = hash_gpu(torch.floor(z.C).int())
        pc_hash = F.sphash(
            torch.cat([
                torch.floor(z.C[:, :3] / x.s[0]).int() * x.s[0],
                z.C[:, -1].int().view(-1, 1)
            ], 1))
        sparse_hash = F.sphash(x.C)
        idx_query = F.sphashquery(pc_hash, sparse_hash)
        counts = F.spcount(idx_query.int(), x.C.shape[0])
        z.additional_features['idx_query'][x.s] = idx_query
        z.additional_features['counts'][x.s] = counts
    else:
        idx_query = z.additional_features['idx_query'][x.s]
        counts = z.additional_features['counts'][x.s]

    inserted_feat = F.spvoxelize(z.F, idx_query, counts)
    new_tensor = SparseTensor(inserted_feat, x.C, x.s)
    new_tensor.cmaps = x.cmaps
    new_tensor.kmaps = x.kmaps

    return new_tensor


# x: SparseTensor, z: PointTensor
# return: PointTensor
def voxel_to_point(x, z, nearest=False):
    if z.idx_query is None or z.weights is None or z.idx_query.get(
            x.s) is None or z.weights.get(x.s) is None:
        off = get_kernel_offsets(2, x.s, 1, device=z.F.device)
        #old_hash = kernel_hash_gpu(torch.floor(z.C).int(), off)
        old_hash = F.sphash(
            torch.cat([
                torch.floor(z.C[:, :3] / x.s[0]).int() * x.s[0],
                z.C[:, -1].int().view(-1, 1)
            ], 1), off)
        pc_hash = F.sphash(x.C.to(z.F.device))
        idx_query = F.sphashquery(old_hash, pc_hash)
        weights = F.calc_ti_weights(z.C, idx_query,
                                    scale=x.s[0]).transpose(0, 1).contiguous()
        idx_query = idx_query.transpose(0, 1).contiguous()
        if nearest:
            weights[:, 1:] = 0.
            idx_query[:, 1:] = -1
        new_feat = F.spdevoxelize(x.F, idx_query, weights)
        new_tensor = PointTensor(new_feat,
                                 z.C,
                                 idx_query=z.idx_query,
                                 weights=z.weights)
        new_tensor.additional_features = z.additional_features
        new_tensor.idx_query[x.s] = idx_query
        new_tensor.weights[x.s] = weights
        z.idx_query[x.s] = idx_query
        z.weights[x.s] = weights

    else:
        new_feat = F.spdevoxelize(x.F, z.idx_query.get(x.s),
                                  z.weights.get(x.s))
        new_tensor = PointTensor(new_feat,
                                 z.C,
                                 idx_query=z.idx_query,
                                 weights=z.weights)
        new_tensor.additional_features = z.additional_features

    return new_tensor